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題名 時間數列之核密度估計探討
Kernel Density Estimation for Time Series
作者 姜一銘
Jiang, I Ming
貢獻者 吳柏林
Wu, B
姜一銘
Jiang, I Ming
關鍵詞 核密度估計
區間帶寬
強混和
幾乎確定
kernel density estimation
bandwidth
strong mixing
almost sure
日期 1996
上傳時間 28-Apr-2016 11:48:29 (UTC+8)
摘要   對樣本資料之機率密度函數f(x)的無母數估計方法,一直是統計推論領域的研究重點之一,而且在通訊理論與圖形辨別上有非常重要的地位。傳統的文獻對密度函數的估計方法大部分著重於獨立樣本的情形。對於時間數列的相關樣本(例如:經濟指標或加權股票指數資料)比較少提到。本文針對具有弱相關性的穩定時間數列樣本,嘗試提出一個核密度估計的方法並探討其性質。
  For a sample data, the nonparametric estimation of a probability density f(x) is always one point of research problem in statistical inference and plays an important role in communication theory and pattern recognition. Traditionally, the literature dealing with density estimation when the observations are independent is extensive. Time series sample with weak dependence, (for example, an economic indicator or a stock market index data), less in this aspect of discussion. Our main purpose is concerned with the estimation of the probability density function f(x) of a stationary time series sample and discusses some properties of this kernel density.
描述 碩士
國立政治大學
統計學系
83354005
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002002788
資料類型 thesis
dc.contributor.advisor 吳柏林zh_TW
dc.contributor.advisor Wu, Ben_US
dc.contributor.author (Authors) 姜一銘zh_TW
dc.contributor.author (Authors) Jiang, I Mingen_US
dc.creator (作者) 姜一銘zh_TW
dc.creator (作者) Jiang, I Mingen_US
dc.date (日期) 1996en_US
dc.date.accessioned 28-Apr-2016 11:48:29 (UTC+8)-
dc.date.available 28-Apr-2016 11:48:29 (UTC+8)-
dc.date.issued (上傳時間) 28-Apr-2016 11:48:29 (UTC+8)-
dc.identifier (Other Identifiers) B2002002788en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/87303-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 83354005zh_TW
dc.description.abstract (摘要)   對樣本資料之機率密度函數f(x)的無母數估計方法,一直是統計推論領域的研究重點之一,而且在通訊理論與圖形辨別上有非常重要的地位。傳統的文獻對密度函數的估計方法大部分著重於獨立樣本的情形。對於時間數列的相關樣本(例如:經濟指標或加權股票指數資料)比較少提到。本文針對具有弱相關性的穩定時間數列樣本,嘗試提出一個核密度估計的方法並探討其性質。zh_TW
dc.description.abstract (摘要)   For a sample data, the nonparametric estimation of a probability density f(x) is always one point of research problem in statistical inference and plays an important role in communication theory and pattern recognition. Traditionally, the literature dealing with density estimation when the observations are independent is extensive. Time series sample with weak dependence, (for example, an economic indicator or a stock market index data), less in this aspect of discussion. Our main purpose is concerned with the estimation of the probability density function f(x) of a stationary time series sample and discusses some properties of this kernel density.en_US
dc.description.tableofcontents 謝辭
摘要
Abstract
Catalog
1. INTRODUCTION-----1
2. PRELIMINARY RESULTS-----4
  2.1 THE MSE AND MISE CRITERIA-----5
  2.2 DERIVATION OF THE OPTIMAL HISTOGRAM-----6
3. MAIN RESULTS-----13
REFERENCES-----24
zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002002788en_US
dc.subject (關鍵詞) 核密度估計zh_TW
dc.subject (關鍵詞) 區間帶寬zh_TW
dc.subject (關鍵詞) 強混和zh_TW
dc.subject (關鍵詞) 幾乎確定zh_TW
dc.subject (關鍵詞) kernel density estimationen_US
dc.subject (關鍵詞) bandwidthen_US
dc.subject (關鍵詞) strong mixingen_US
dc.subject (關鍵詞) almost sureen_US
dc.title (題名) 時間數列之核密度估計探討zh_TW
dc.title (題名) Kernel Density Estimation for Time Seriesen_US
dc.type (資料類型) thesisen_US